Xiaowei Gu

• Doctor of Philosophy
• Senior Lecturer at University of Surrey

In contemporary organizational research, when dealing with large heterogeneous datasets and complex relationships, statistical modeling focused on developing substantive explanations typically results in low predictive accuracy. In contrast, machine learning (ML) exhibits remarkable strength for prediction, but suffers from an unexplainable analyti...

High-dimensional data classification is widely considered as a challenging task in machine learning due to the so-called “curse of dimensionality”. In this paper, a novel multilayer jointly evolving and compressing fuzzy neural network (MECFNN) is proposed to learn highly compact multi-level latent representations from high-dimensional data. As a m...

In this paper, a novel autonomous centreless algorithm is proposed for data partitioning. The proposed algorithm firstly constructs the nearest neighbour affinity graph and identifies the local peaks of data density to build micro-clusters. Unlike the vast majority of partitional clustering algorithms, the proposed algorithm does not rely on single...

This paper proposes a dynamic evolving fuzzy system (DEFS) for streaming data prediction. DEFS utilises the enhanced data potential and prediction errors of individual local models as the main criteria for fuzzy rule generation. A vital feature of the proposed system is its novel rule merging scheme that can self-adjust its tolerance towards the de...

Nowadays, cyber-attacks have become a common and persistent issue affecting various human activities in modern societies. Due to the continuously evolving landscape of cyber-attacks and the growing concerns around “black box” models, there has been a strong demand for novel explainable and interpretable intrusion detection systems with online learn...

Fuzzy systems offer a formal and practically popular methodology for modelling nonlinear problems with inherent uncertainties, entailing strong performance and model interpretability. Particularly, semi-supervised boosting is widely recognised as a powerful approach for creating stronger ensemble classification models in the absence of sufficient l...

Tropical cyclones (TCs), with an intensive wind pump impact, induce sea surface temperature cooling (SSTC) on the upper ocean. SSTC is a pronounced indicator to reveal TC evolution and oceanic conditions. However, there are few effective methods for accurately approximating the amplitude of the spatial structure of TC-induced SSTC. This study propo...

It is widely recognised that learning systems have to go deeper to exchange for more powerful representation learning capabilities in order to precisely approximate nonlinear complex problems. However, the best known computational intelligence approaches with such characteristics, namely, deep neural networks, are often criticised for lacking trans...

Plain Language Summary While many studies have been devoted to understanding the processes and mechanisms underlying the sea surface temperature (SST) cooling induced by tropical cyclones (TCs), few studies have attempted to predict the spatial and temporal evolution of the sea surface temperature (SST) cooling triggered by TCs. In this study, we p...

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transfer...

Sea surface height anomaly (SSHA) induced by tropical cyclones (TCs) is closely associated with oscillations and is a crucial proxy for thermocline structure and ocean heat content in the upper ocean. The prediction of TC-induced SSHA, however, has been rarely investigated. This study presents a new composite analysis-based random forest (RF) appro...

As one of the three pillars in computational intelligence, fuzzy systems are a powerful mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy systems take the form of linguistic IF-THEN fuzzy rules that are easy to understand for human. In this sense, fuzzy inference mechanisms have been developed to mimic human r...

Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this article, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the interclass overlaps in a natural way and better capture the underly...

Mobile medical app evaluation can be modelled as a multi-attribute decision-making (MADM) problem with multiple assessment attributes. Due to the increasing complexity and high uncertainty of decision environments, numerical numbers and/or traditional fuzzy sets may not be appropriate to model attribute information of mobile medical apps. In additi...

In this paper, a novel approach to the self-organization of hierarchical prototype-based classifiers from data is proposed. The approach recursively partitions the data at multiple levels of granularity into shape-free clusters of different sizes, resembling Voronoi tessellation, and naturally aggregates the resulting cluster medoids into a multi-l...

Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated strong performance on many real-world problems concerning data stream classification, while offering high model transparency and interpretability thanks to their prototype-based nature. Zero-order EISs typically learn prototypes by clustering streaming data onl...

Adaptive boosting (AdaBoost) is a widely used technique to construct a stronger ensemble classifier by combining a set of weaker ones. Zero-order fuzzy inference systems (FISs) are very powerful prototype-based predictive models for classification, offering both great prediction precision and high user-interpretability. However, the use of zero-ord...

We propose a new interval type-2 fuzzy (IT2F) programming method for risky multicriteria decision-making (MCDM) problems with IT2F truth degrees, where the criteria exhibit a heterogeneous relationship and decision-makers behave according to bounded rationality. First, we develop a technique to calculate the Banzhaf-based overall perceived utility...

Remote sensing scene classification plays a critical role in a wide range of real-world applications. Technically, however, scene classification is an extremely challenging task due to the huge complexity in remotely sensed scenes, and the difficulty in acquiring labelled data for model training such as supervised deep learning. To tackle these iss...

As a powerful tool for data streams processing, the vast majority of existing evolving intelligent systems (EISs) learn prediction models from data in a supervised manner. However, high-quality labelled data can be difficult to obtain in many real-world classification applications concerning data streams, though unlabelled data is plentiful. To ove...

Vein contraction and venous compression typically caused by low temperature and excessive placement pressure can blur the captured finger vein images and severely impair the quality of extracted features. To improve the quality of captured finger vein image, this paper proposes a 26-layer generator network constrained by Neighbors-based Binary Patt...

In this paper, a novel self-adaptive fuzzy learning (SAFL) system is proposed for streaming data prediction. SAFL self-learns from data streams a predictive model composed of a set of prototype-based fuzzy rules, with each of which representing a certain local data distribution, and continuously self-evolves to follow the changing data patterns in...

Cloud removal in optical remote sensing imagery is essential for many Earth observation applications.Due to the inherent imaging geometry features in satellite remote sensing, it is impossible to observe the ground under the clouds directly; therefore, cloud removal algorithms are always not perfect owing to the loss of ground truth. Passenger airc...

In this paper, a new data-driven autonomous fuzzy clustering (AFC) algorithm is proposed for static data clustering. Employing a Gaussian-type membership function, AFC firstly uses all the data samples as micro-cluster medoids to assign memberships to each other and obtains the membership matrix. Based on this, AFC chooses these data samples that r...

Prototype-based approaches generally provide better explainability and are widely used for classification. However, the majority of them suffer from system obesity and lack transparency on complex problems. In this paper, a novel classification approach with a multi-layered system structure self-organized from data is proposed. This approach is abl...

Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction me...

An evolving intelligent system (EIS) is able to self-update its system structure and meta-parameters from streaming data. However, since the majority of EISs are implemented on a single-model architecture, their performances on large-scale, complex data streams are often limited. To address this deficiency, a novel self-organizing fuzzy inference e...

Large-scale {(large-area)}, fine spatial resolution satellite sensor images are valuable data sources for Earth observation while not yet fully exploited by research communities for practical applications. Often, such images exhibit highly complex geometrical structures and spatial patterns, and distinctive characteristics of multiple land-use cate...

Small area change detection from synthetic aperture radar (SAR) is a highly challenging task. In this paper, a robust unsupervised approach is proposed for small area change detection from multi-temporal SAR images using deep learning. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can...

In recent years, numerous techniques have been proposed for human activity recognition (HAR) from images and videos. These techniques can be divided into two major categories: handcrafted and deep learning. Deep Learning-based models have produced remarkable results for HAR. However, these models have several shortcomings, such as the requirement f...

A novel self-organizing fuzzy proportional-integral-derivative (SOF-PID) control system is proposed in this paper. The proposed system consists of a pair of control and reference models, both of which are implemented by a first-order autonomous learning multiple model (ALMMo) neuro-fuzzy system. The SOF-PID controller self-organizes and self-update...

This paper proposes a new extended zero-order Autonomous Learning Multiple-Model (ALMMo-0*) neuro-fuzzy approach in order to classify different heart disorders through sounds. ALMMo-0* is build upon the recently introduced ALMMo-0. In this paper, ALMMo-0 is extended by adding a pre-processing structure which improves the performance of the proposed...

This paper introduces a novel self-training hierarchical prototype-based approach for semi-supervised classification. The proposed approach firstly identifies meaningful prototypes from labelled samples at multiple levels of granularity and, then, self-organizes a highly transparent, multi-layered recognition model by arranging them in a form of py...

In order to tackle high-dimensional, complex problems, learning models have to go deeper. In this paper, a novel multi-layer ensemble learning model with firrst-order evolving fuzzy systems as its building blocks is introduced. The proposed approach can effectively learn from streaming data on a sample-by-sample basis and self-organizes its multi-l...

Pioneering the traditional fuzzy rule-based (FRB) systems, deep rule-based (DRB) classifiers are able to offer both human-level performance and transparent system structure on image classification problems by integrating zero-order fuzzy rule base with a multi-layer image-processing architecture that is typical for the deep learning paradigm. Nonet...

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%‐80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validatio...

The antecedent and consequent parts of a first-order evolving intelligent system (EIS) determine the validity of the learning results and overall system performance. Nonetheless, the state-of-the-art techniques mostly stress on the novelty from the system identification point of view but pay less attention to the optimality of the learned parameter...

This paper proposes a new approach that is based on the recently introduced semi-supervised deep rule-based classifier for remote sensing scene classification. The proposed approach employs a pre-trained deep convoluational neural network as the feature descriptor to extract high-level discriminative semantic features from the sub-regions of the re...

A novel self-organizing fuzzy proportional-integral-derivative (SOF-PID) control system is proposed in this paper. The proposed system consists of a pair of control and reference models, both of which are implemented by a first-order autonomous learning multiple model (ALMMo) neuro-fuzzy system. The SOF-PID controller self-organizes and self-update...

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50% to 80%) is used for training and the rest for validation. In many problems, however, the data is highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validat...

Optimality of the premise, IF part is critical to a zero-order evolving intelligent system (EIS) because this part determines the validity of the learning results and overall system performance. Nonetheless, a systematic analysis of optimality has not been done yet in the state-of-the-art works. In this paper, we use the recently introduced self-or...

In this paper, a novel hierarchical prototype-based approach for classification is proposed. This approach is able to perceive the data space and derive the multimodal distributions from streaming data at different levels of granularity in an online manner, based on which it further identifies meaningful prototypes to self-organize and self-evolve...

In this paper, a detailed mathematical analysis of the optimality of the premise and consequent parts of the recently introduced first-order Autonomous Learning Multi-Model (ALMMo) neuro-fuzzy system is conducted. A novel self-boosting algorithm for structure- and parameter-optimization is, then, introduced to the ALMMo, which results in the self-b...

In this paper, we offer a method aiming to minimize the role of distance metric used in clustering. It is well known that distance metrics used in clustering algorithms heavily influence the end results and also make the algorithms sensitive to imbalanced attribute/feature scales. To solve these problems, a new clustering algorithm using a per-attr...

In this chapter, a new type of deep rule-based (DRB) classifier with a multi-layer architecture is presented for image classification, which combines the computer vision techniques with a massively parallel set of zero-order fuzzy rules as its learning engine. With its prototype-based nature, the DRB classifiers are able to identify a transparent a...

In this chapter, the empirical approach to the problem of anomaly detection is presented, which is free from the pre-defined model and user-and problem-specific parameters and is data driven. The well-known Chebyshev inequality has been simplified by using the standardized eccentricity. An autonomous anomaly detection method is proposed, which is c...

In this chapter, we will describe the fundamentals of the proposed new “empirical” approach as a systematic methodology with its nonparametric quantities derived entirely from the actual data with no subjective and/or problem-specific assumptions made. It has a potential to be a powerful extension of (and/or alternative to) the traditional probabil...

This chapter provides a detailed introduction to the basic concepts and the general principles of the fuzzy sets and systems theory. Three major types of FRB systems are also covered and their differences are analyzed. The design of FRB systems is also covered. This chapter further moves on to the ANNs, which include the feedforward neural networks...

In this chapter, the algorithm summary of the proposed autonomous anomaly detection (AAD) algorithm described in Chap. 6 is provided. Numerical examples based on both the synthetic and benchmark datasets are presented for evaluating the performance of the AAD algorithm. Well-known traditional anomaly detection approaches are used for a further comp...

In this chapter, the algorithm summary of the main procedure of the deep rule-based (DRB) classifier described in Chap. 9 is provided. Numerical examples based on popular benchmark image sets including, handwritten digits recognition, remote sensing scene classification, face recognition and object recognition, etc., are presented for evaluating th...

In this chapter, the algorithm summaries of the autonomous learning multi-model systems of zero-order (ALMMo-0) and first-order (ALMMo-1) described in Chap. 8 are provided. Numerical examples based on well-known benchmark datasets are presented for evaluating the classification performance of the ALMMo-0 and ALMMo-1 systems. Real-world problems are...

In this chapter, the concepts and general principles of the empirical fuzzy sets and the fuzzy rule-based (FRB) systems based on them, named empirical FRB systems are presented, and two approaches for identifying empirical FRB systems, namely, the subjective one, which is based on human expertise, and the objective one, which is based on the autono...

In this chapter, an overview of the theory of probability, statistical and machine learning is made covering the main ideas and the most popular and widely used methods in this area. As a starting point, the randomness and determinism as well as the nature of the real-world problems are discussed. Then, the basic and well-known topics of the tradit...

In this chapter, a new empirical approach, named autonomous data partitioning, is proposed to partition the data autonomously by creating a Voronoi tessellation around the objectively identified prototypes to form data clouds, which transform the large amount of raw data into a much smaller (manageable) number of more representative aggregations wi...

In this chapter, the algorithm summary of the main procedure of the semi-supervised deep rule-based (SS_DRB) classifier described in Chap. 9 is provided, which serves as a powerful extension of the DRB classifier. The offline learning process of the SS_DRB classifier is illustrated and the performance of the SS_DRB algorithm is evaluated based on b...

In this chapter, the algorithm summaries of both, the offline and evolving versions of the proposed autonomous data partitioning (ADP) algorithm described in chapter 7 are provided. Numerical examples based on well-known benchmark datasets are presented for evaluating the performance of the ADP algorithm on data partitioning. Furthermore, numerical...

In this chapter, the Autonomous Learning Multi-Model (ALMMo) systems are introduced, which are based on the AnYa type neuro-fuzzy systems and can be seen as an universal self-developing, self-evolving, stable, locally optimal proven universal approximators. This chapter starts with the general concepts and principles of the zero- and first-order AL...

The complex nature of the foreign exchange (FOREX) market along with the increased interest towards the currency exchange market has prompted extensive research from various academic disciplines. With the inclusion of more in-depth analysis and forecasting methods, traders will be able to make an informed decision when trading. Therefore, an approa...

The package contains: 1. The advanced Autonomous Data Partitioning algorithm (offline version); 2. A demo. This version supports 1. multiple types of distance; 2. data partitioning under different levels of granularity. Please notice that this code is licensed under GNU GENERAL PUBLIC LICENSE Version 3.

Future intelligent machines will be more human-friendly and human-like, while offering much higher throughput and automation, thus augmenting our (human) capabilities. Anthropomorphic machine learning is an emerging direction for future development in artificial intelligence (AI) and data science. This revolutionary shift offers human-like abilitie...

The package contains: 1. The recently introduced autonomous learning algorithm- Autonomous Learning Multi-Model System; 2. A demo for classification; 3. A demo for regression. Please notice that this code is licensed under GNU GENERAL PUBLIC LICENSE Version 3

The package contains: 1. The recently introduced classification algorithm- Autonomous Learning Multi-Model Classifier of Zero Order; 2. A demo. Please notice that this code is licensed under GNU GENERAL PUBLIC LICENSE Version 3

In this paper, a new type of multilayer rule-based classifier is proposed and applied to image classification problems. The proposed approach is entirely data-driven and fully automatic. It is generic and can be applied to various classification and prediction problems, but in this paper we focus on image processing, in particular. The core of the...

In this paper, we propose a fully autonomous, local-modes-based data partitioning algorithm, which is able to automatically recognize local maxima of the data density from empirical observations and use them as focal points to form shape-free data clouds, i.e. a form of Voronoi tessellation. The method is free from user- and problem- specific param...

The package contains: 1. The recently introduced self-organising fuzzy logic (SOF) classifier; 2. A demo.

In this paper, we present a self-organising nonparametric fuzzy rule-based classifier. The proposed approach identifies prototypes from the observed data through an offline training process and uses them to build a 0-order AnYa type fuzzy rule-based system for classification. Once primed offline, it is able to continuously learn from the streaming...

In this paper, a semi-supervised learning approach based on a deep rule-based (DRB) classifier is introduced. With its unique prototype-based nature, the semi-supervised DRB (SSDRB) classifier is able to generate human interpretable IF…THEN…rules through the semi-supervised learning process in a self-organising and highly transparent manner. It sup...

In this letter, we propose a new approach for remote sensing scene classification by creating an ensemble of the recently introduced massively parallel deep (fuzzy) rule-based (DRB) classifiers trained with different levels of spatial information separately. Each DRB classifier consists of a massively parallel set of human-interpretable, transparen...

The package contains: 1. The recently introduced Self-Organised Direction Aware Data Partitioning Algorithm (SODA); 2. A demo for offline data partitioning/clustering; 3. A demo for conducting hybrid between the offline prime and the evolving extension.

Evolving fuzzy systems (EFSs) are now well developed and widely used thanks to their ability to self-adapt both their structures and parameters online. Since the concept was firstly introduced two decades ago, many different types of EFSs have been successfully implemented. However, there are only very few works considering the stability of the EFS...

The package contains: 1. The recently introduced classification algorithm- Autonomous Learning Multi-Model Classifier of Zero Order; 2. A demo.

The package contains: 1. The recently introduced autonomous learning algorithm- Autonomous Learning Multi-Model System; 2. A demo for classification; 3. A demo for regression.

In this paper, an approach to autonomous learning of a multi-model system from streaming data, named ALMMo, is proposed. The proposed approach is generic and can easily be applied also to probabilistic or other types of local models forming multi-model systems. It is fully data-driven and its structure is decided by the nonparametric data clouds ex...

Based on a critical analysis of data analytics and its foundations, we propose a functional approach to estimate data ensemble properties, which is based entirely on the empirical observations of discrete data samples and the relative proximity of these points in the data space and hence named empirical data analysis (EDA). The ensemble functions i...

In this paper, a novel online clustering approach called Parallel_TEDA is introduced for processing high frequency streaming data. This newly proposed approach is developed within the recently introduced TEDA theory and inherits all advantages from it. In the proposed approach, a number of data stream processors are involved, which collaborate with...

In this paper, we introduce a new form of describing fuzzy sets (FSs) and a new form of fuzzy rule-based (FRB) systems, namely, empirical fuzzy sets (εFSs) and empirical fuzzy rule-based (εFRB) systems. Traditionally, the membership functions (MFs), which are the key mathematical representation of FSs, are designed subjectively or extracted from th...

In order to address high dimensional problems, a new ‘direction-aware’ metric is introduced in this paper. This new distance is a combination of two components: (1) the traditional Euclidean distance and (2) an angular/directional divergence, derived from the cosine similarity. The newly introduced metric combines the advantages of the Euclidean me...

In this paper, a novel fully data-driven algorithm, named Self-Organised Direction Aware (SODA) data partitioning and forming data clouds is proposed. The proposed SODA algorithm employs an extra cosine similarity-based directional component to work together with a traditional distance metric, thus, takes the advantages of both the spatial and angu...

In this article, we describe an approach to computational modeling and autonomous learning of the perception of sensory inputs by individuals. A hierarchical process of summarization of heterogeneous raw data is proposed. At the lower level of the hierarchy, the raw data autonomously form semantically meaningful concepts. Instead of clustering base...

In this paper, we propose an approach to data analysis, which is based entirely on the empirical observations of discrete data samples and the relative proximity of these points in the data space. At the core of the proposed new approach is the typicality—an empirically derived quantity that resembles probability. This nonparametric measure is a no...

In this paper, we propose a method to detect anomalous behaviour using heterogenous data. This method detects anomalies based on the recently introduced approach known as Recursive Density Estimation (RDE) and the so called eccentricity. This method does not require prior assumptions to be made on the type of the data distribution. A simplified for...

Aiming at the problem of blind estimation of Pseudo-random (PN) sequences in multi-user long scrambling code direct sequence spread spectrum (LSC-DSSS) signals, in this paper, we proposed a novel PN sequences estimation method based on Fast-ICA algorithm and third-order statistics. The received signal is firstly divided twice into segments and then...

In this paper, a new data partitioning algorithm, named “local modes-based data partitioning”, is proposed. This algorithm is entirely data-driven and free from any user input and prior assumptions. It automatically derives the modes of the empirically observed density of the data samples and results in forming parameter-free data clouds. The ident...